LEDTD: Local edge direction and texture descriptor for face recognition

Abstract

A good image representation is critical to face recognition task. Recently, eight-direction Kirsch masks based image descriptors, e.g. local directional pattern (LDP), local sign directional pattern (LSDP), have been devised and shown competitive results than conventional LBP descriptor. However, these methods may lose or do not fully explore valuable texture information of the image. To remedy this drawback, a novel discriminative image descriptor, namely local edge direction and texture descriptor (LEDTD) is proposed in this paper. LEDTD differs from the existing Kirsch based methods in a manner that it not only considers image edge direction information but also extracts image texture feature by encoding the edge response directions of center and its neighborhood pixels by employing local XOR binary coding strategy. Finally, edge direction and texture features are integrated to form the image feature vector. Extensive performance evaluations on four benchmark face databases show that the proposed approach yields a better performance in terms of the recognition rate as well as robustness to the noise compared with the state of the art methods.